R version 2.6.1 (2007-11-26)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(-12.7,-2.4,7.1,-3.9,9.5,5,-16.1,-10.8,7,13.6,8.1,-8.1,4.9,-0.8,4.3,4,1.5,5.4,-11.3,-16.4,-2,8.9,-7.2,-18,1.3,6.3,-6,2.8,2,5.1,-7.6,-18.6,5.8,20.3,0.7,-11.2,-5.7,-0.1,3.4,3.3,-1.2,4.2,-8.8,-25.3,8.5,14.5,-3.1,-10.4,-2.9,0.3,22.6,15.4,9,29.1,2.8,-3.8,27.7,28.9,26.5,19.8,13.2,14.1,34.1,30,21.8,32.1,5.3,3,17.1,26.3,38.1,19.5,38,35.5,78.6,62.2,76.9,104.9,32.2,42.5,64.3,74.9,75.4,43,58.7,55.4,76.6,63.3,78.9,82.7)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '1'
> par7 = '1'
> par6 = '3'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '1'
> par1 = '12'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> par1 <- as.numeric(par1) #cut off periods
> par2 <- as.numeric(par2) #lambda
> par3 <- as.numeric(par3) #degree of non-seasonal differencing
> par4 <- as.numeric(par4) #degree of seasonal differencing
> par5 <- as.numeric(par5) #seasonal period
> par6 <- as.numeric(par6) #p
> par7 <- as.numeric(par7) #q
> par8 <- as.numeric(par8) #P
> par9 <- as.numeric(par9) #Q
> if (par10 == 'TRUE') par10 <- TRUE
> if (par10 == 'FALSE') par10 <- FALSE
> if (par2 == 0) x <- log(x)
> if (par2 != 0) x <- x^par2
> lx <- length(x)
> first <- lx - 2*par1
> nx <- lx - par1
> nx1 <- nx + 1
> fx <- lx - nx
> if (fx < 1) {
+ fx <- par5
+ nx1 <- lx + fx - 1
+ first <- lx - 2*fx
+ }
> first <- 1
> if (fx < 3) fx <- round(lx/10,0)
> (arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))

Call:
arima(x = x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8, 
    par4, par9), period = par5), include.mean = par10, method = "ML")

Coefficients:
         ar1     ar2     ar3      ma1     sar1
      0.0836  0.2425  0.4332  -0.5842  -0.2914
s.e.  0.2051  0.1420  0.1294   0.1942   0.1557

sigma^2 estimated as 74.54:  log likelihood = -233.27,  aic = 478.55
> (forecast <- predict(arima.out,fx))
$pred
Time Series:
Start = 79 
End = 90 
Frequency = 1 
 [1]  71.42445  80.59235 105.60243 113.08904 127.72840 115.85889 129.27632
 [8] 131.76729 170.42404 159.73691 170.22867 194.78390

$se
Time Series:
Start = 79 
End = 90 
Frequency = 1 
 [1]  8.633862  9.650529 11.386786 14.440116 16.348421 18.662798 21.195507
 [8] 23.407366 25.760120 28.109274 30.348220 32.609833

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 79 
End = 90 
Frequency = 1 
 [1]  54.50208  61.67731  83.28433  84.78641  95.68549  79.27980  87.73312
 [8]  85.88886 119.93421 104.64273 110.74616 130.86863
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 79 
End = 90 
Frequency = 1 
 [1]  88.34682  99.50739 127.92053 141.39167 159.77130 152.43797 170.81951
 [8] 177.64573 220.91388 214.83109 229.71118 258.69917
> if (par2 == 0) {
+ x <- exp(x)
+ forecast$pred <- exp(forecast$pred)
+ lb <- exp(lb)
+ ub <- exp(ub)
+ }
> if (par2 != 0) {
+ x <- x^(1/par2)
+ forecast$pred <- forecast$pred^(1/par2)
+ lb <- lb^(1/par2)
+ ub <- ub^(1/par2)
+ }
> if (par2 < 0) {
+ olb <- lb
+ lb <- ub
+ ub <- olb
+ }
> (actandfor <- c(x[1:nx], forecast$pred))
 [1] -12.70000  -2.40000   7.10000  -3.90000   9.50000   5.00000 -16.10000
 [8] -10.80000   7.00000  13.60000   8.10000  -8.10000   4.90000  -0.80000
[15]   4.30000   4.00000   1.50000   5.40000 -11.30000 -16.40000  -2.00000
[22]   8.90000  -7.20000 -18.00000   1.30000   6.30000  -6.00000   2.80000
[29]   2.00000   5.10000  -7.60000 -18.60000   5.80000  20.30000   0.70000
[36] -11.20000  -5.70000  -0.10000   3.40000   3.30000  -1.20000   4.20000
[43]  -8.80000 -25.30000   8.50000  14.50000  -3.10000 -10.40000  -2.90000
[50]   0.30000  22.60000  15.40000   9.00000  29.10000   2.80000  -3.80000
[57]  27.70000  28.90000  26.50000  19.80000  13.20000  14.10000  34.10000
[64]  30.00000  21.80000  32.10000   5.30000   3.00000  17.10000  26.30000
[71]  38.10000  19.50000  38.00000  35.50000  78.60000  62.20000  76.90000
[78] 104.90000  71.42445  80.59235 105.60243 113.08904 127.72840 115.85889
[85] 129.27632 131.76729 170.42404 159.73691 170.22867 194.78390
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 79 
End = 90 
Frequency = 1 
 [1] 0.1208811 0.1197450 0.1078269 0.1276880 0.1279936 0.1610821 0.1639551
 [8] 0.1776417 0.1511531 0.1759723 0.1782791 0.1674154
> postscript(file="/var/www/html/rcomp/tmp/1y0qa1201180595.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
> box()
> par(opar)
> dev.off()
null device 
          1 
> prob.dec <- array(NA, dim=fx)
> prob.sdec <- array(NA, dim=fx)
> prob.ldec <- array(NA, dim=fx)
> prob.pval <- array(NA, dim=fx)
> perf.pe <- array(0, dim=fx)
> perf.mape <- array(0, dim=fx)
> perf.se <- array(0, dim=fx)
> perf.mse <- array(0, dim=fx)
> perf.rmse <- array(0, dim=fx)
> for (i in 1:fx) {
+ locSD <- (ub[i] - forecast$pred[i]) / 1.96
+ perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
+ perf.mape[i] = perf.mape[i] + abs(perf.pe[i])
+ perf.se[i] = (x[nx+i] - forecast$pred[i])^2
+ perf.mse[i] = perf.mse[i] + perf.se[i]
+ prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
+ prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
+ prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
+ prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
+ }
> perf.mape = perf.mape / fx
> perf.mse = perf.mse / fx
> perf.rmse = sqrt(perf.mse)
> postscript(file="/var/www/html/rcomp/tmp/207du1201180595.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
> dum <- forecast$pred
> dum[1:12] <- x[(nx+1):lx]
> lines(dum, lty=1)
> lines(ub,lty=3)
> lines(lb,lty=3)
> dev.off()
null device 
          1 
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'Y[t]',1,header=TRUE)
> a<-table.element(a,'F[t]',1,header=TRUE)
> a<-table.element(a,'95% LB',1,header=TRUE)
> a<-table.element(a,'95% UB',1,header=TRUE)
> a<-table.element(a,'p-value<br />(H0: Y[t] = F[t])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
> mylab <- paste('P(F[t]>Y[',nx,sep='')
> mylab <- paste(mylab,'])',sep='')
> a<-table.element(a,mylab,1,header=TRUE)
> a<-table.row.end(a)
> for (i in (nx-par5):nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.row.end(a)
+ }
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(x[nx+i],4))
+ a<-table.element(a,round(forecast$pred[i],4))
+ a<-table.element(a,round(lb[i],4))
+ a<-table.element(a,round(ub[i],4))
+ a<-table.element(a,round((1-prob.pval[i]),4))
+ a<-table.element(a,round((1-prob.dec[i]),4))
+ a<-table.element(a,round((1-prob.sdec[i]),4))
+ a<-table.element(a,round((1-prob.ldec[i]),4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/3akzo1201180595.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'% S.E.',1,header=TRUE)
> a<-table.element(a,'PE',1,header=TRUE)
> a<-table.element(a,'MAPE',1,header=TRUE)
> a<-table.element(a,'Sq.E',1,header=TRUE)
> a<-table.element(a,'MSE',1,header=TRUE)
> a<-table.element(a,'RMSE',1,header=TRUE)
> a<-table.row.end(a)
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(perc.se[i],4))
+ a<-table.element(a,round(perf.pe[i],4))
+ a<-table.element(a,round(perf.mape[i],4))
+ a<-table.element(a,round(perf.se[i],4))
+ a<-table.element(a,round(perf.mse[i],4))
+ a<-table.element(a,round(perf.rmse[i],4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/45m881201180595.tab") 
> 
> system("convert tmp/1y0qa1201180595.ps tmp/1y0qa1201180595.png")
> system("convert tmp/207du1201180595.ps tmp/207du1201180595.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.262   0.562   2.361